In today’s world, there is a lot of importance to Data Science. This is a field which involves scientific methods, processes, algorithms, and systems to extract knowledge and insights from raw data in various forms, both structured and unstructured data. This gives us meaningful data as output from the raw data and the unstructured data. Data science has a huge demand and provides a large number of career opportunities for demand. The following books provide a simple and easy understanding of the new concepts and applications.

#1 – Python Data Science Handbook

Essential Tools for Working with Data

Author: Jake VanderPlas

Book Review:

The book is ideally suited to those that already know the basics of the Python language or already know how to program in another language like R or Julia and want to learn how to use Python for data science. This book explains all the needs of the entire Data Science process from getting data, exploring data and communicating and visualizing the results.

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#2 – Data Science (MIT Press Essential Knowledge series)

Author: John D. Kelleher and Brendan Tierney

Book Review:

The main aim of this book is to improve decision making through analysis of data. This introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems.

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#3 – R for Data Science

Import, Tidy, Transform, Visualize, and Model Data

Author: Hadley Wickham and Garrett Grolemund

Book Review:

This book will give a clear understanding of discovering natural laws in the structure of data. This will tell you how to use the R Programming language for data analysis. This also tells how to clean the data draw plots and how to use the grammar of graphics, literate programming, and reproducible research to save time and also many other things.

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#4 – Storytelling with Data

A Data Visualization Guide for Business Professionals

Author: Cole Nussbaumer Knaflic

Book Review:

This book mainly explains the fundamentals of data visualization and how to communicate effectively with data. By this book, you will be able to find out which is the crucial point for your data. This tells how to go beyond the conventional tools to reach the root of your data and how to create an informative and compelling story.

Key Takeaways For This Top Data Science Book:

Understanding the situation and audience.

Identifying the important point of the data.

Concepts of design in data visualization.

Power of storytelling to help your message resonate with your audience.

#5 – Data Science from Scratch

First Principles with Python

Author: Joel Grus

Book Review:

The author has clearly explained the important data science tools and the algorithms and how they can be implemented from scratch. This book contains the actual algorithms for those machine learning models along with the theory and mathematics in it.

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#6 – Data Science for Business

What You Need to Know about Data Mining and Data-Analytic Thinking

Author: Foster Provost and Tom Fawcett

Book Review:

This book explains the fundamental principles of data science, and also the data-analytic thinking which is required for obtaining important knowledge and business information from the data. This also helps in the understanding of the techniques which are applied nowadays. This book also provides examples for the real world business problems to explain the principles.

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#7 – Data Smart

Using Data Science to Transform Information into Insight

Author: John W Foreman

Book Review:

The author clearly explains how to convert the raw data into the actionable insight. The author also explained how to do it with the Spreadsheet. This will also help you in learning the analytical techniques, the mathematics and the magic behind the big data. Each chapter in the book will cover a different technique in a spreadsheet like mathematical optimization, data mining in graphs, moving from spreadsheets to R programming language and many other things.

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#8 – Practical Statistics for Data Scientists

50 Essential Concepts

Author: Peter Bruce

Book Review:

Statistics also plays an important role in Data Science. In this book, the author has clearly explained how to apply various statistical methods to data science in the present, and also how to avoid them which are in wrong use and gives you output on what’s important and what’s not. If you’re good with the R programming language and have some knowledge to statistics, this quick reference builds the gap to a larger extent in the readable format.

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#9 – Numsense! Data Science for the Layman

No Math Added

Author: Annalyn Ng and Kenneth Soo

Book Review:

This book gives a clear understanding of the data science and the algorithms which are used. Every algorithm is clearly explained in this book. There are many concepts which all are covered in this book like Neural Networks, Social Network Analysis, Decision Trees and Random Forests, Clustering and also many more.

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#10 – Practical Data Science with R

Author: Nina Zumel and John Mount

Book Review:

This book clearly explains the practical examples and fundamental principles of the data science with the programming language R. This will help in applying the R programming language and statistical analysis techniques to carefully-explained examples based in marketing, business intelligence, and decision support, while learning how to create instrumentation, design experiments such as A/B tests, and accurately present data to audiences of all levels.

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